AI-Powered 3D Data Fusion System for Vegetation Risk Assessment in Distribution Networks

Vegetation is a leading cause of outages in distribution systems. Traditional vegetation management still relies on costly manual patrols and fixed trimming cycles. This paper develops an end-to-end, AI-driven framework to address vegetation-related outage risk in distribution networks by operating directly on three-dimensional light detection and ranging (LiDAR) point clouds. A RandLA-Net–based semantic segmentation model is trained to automatically separate vegetation, buildings, poles, and power lines. A canopy height model and watershed pipeline is then used to isolate individual trees and derive per-tree attributes such as height, crown radius, and minimum three-dimensional clearance to conductors and poles. On top of these geometric descriptors, we propose a tree-level risk-scoring…